Description Usage Arguments Details Value Note Author(s) References See Also Examples
The DEL()
function defines the Delaporte distribution, a three parameter discrete distribution, for a gamlss.family
object to be used
in GAMLSS fitting using the function gamlss()
.
The functions dDEL
, pDEL
, qDEL
and rDEL
define the density, distribution function, quantile function and random
generation for the Delaporte DEL()
, distribution.
1 2 3 4 5 6 7 | DEL(mu.link = "log", sigma.link = "log", nu.link = "logit")
dDEL(x, mu=1, sigma=1, nu=0.5, log=FALSE)
pDEL(q, mu=1, sigma=1, nu=0.5, lower.tail = TRUE,
log.p = FALSE)
qDEL(p, mu=1, sigma=1, nu=0.5, lower.tail = TRUE,
log.p = FALSE, max.value = 10000)
rDEL(n, mu=1, sigma=1, nu=0.5, max.value = 10000)
|
mu.link |
Defines the |
sigma.link |
Defines the |
nu.link |
Defines the |
x |
vector of (non-negative integer) quantiles |
mu |
vector of positive mu |
sigma |
vector of positive dispersion parameter |
nu |
vector of nu |
p |
vector of probabilities |
q |
vector of quantiles |
n |
number of random values to return |
log, log.p |
logical; if TRUE, probabilities p are given as log(p) |
lower.tail |
logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x] |
max.value |
a constant, set to the default value of 10000 for how far the algorithm should look for q |
The probability function of the Delaporte distribution is given by
f(y|mu,sigma,nu)=(exp(-mu*nu)/Gamma(1/sigma))*[1+mu*sigma*(1-nu)]^(-1/sigma) S
where
S=Sum(Per(y,j))*((mu^y)*(nu^{y-j})/y!) *[1+(1/(sigma*(1-nu)))]^j Gamma((1/sigma)*j)
for y=0,1,2,... where mu>0 , σ>0 and 0<nu<1. This distribution is a parametrization of the distribution given by Wimmer and Altmann (1999) p 515-516 where a=mu*nu, 1/sigma and p=[1+mu*nu*(1-nu)]^(-1)
Returns a gamlss.family
object which can be used to fit a Delaporte distribution in the gamlss()
function.
The mean of Y is given by E(Y)=mu and the variance by V(Y)=mu+mu^2*sigma*(1-nu)^2.
Rigby, R. A., Stasinopoulos D. M. and Marco Enea
Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), Appl. Statist., 54, part 3, pp 507-554.
Rigby, R. A., Stasinopoulos D. M. and Akantziliotou, C. (2006) Modelling the parameters of a family of mixed Poisson distributions including the Sichel and Delaptorte. Submitted for publication.
Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2003) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).
Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, http://www.jstatsoft.org/v23/i07.
Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.
Wimmer, G. and Altmann, G (1999). Thesaurus of univariate discrete probability distributions . Stamn Verlag, Essen, Germany
1 2 3 4 5 6 7 8 9 10 11 | DEL()# gives information about the default links for the Delaporte distribution
#plot the pdf using plot
plot(function(y) dDEL(y, mu=10, sigma=1, nu=.5), from=0, to=100, n=100+1, type="h") # pdf
# plot the cdf
plot(seq(from=0,to=100),pDEL(seq(from=0,to=100), mu=10, sigma=1, nu=0.5), type="h") # cdf
# generate random sample
tN <- table(Ni <- rDEL(100, mu=10, sigma=1, nu=0.5))
r <- barplot(tN, col='lightblue')
# fit a model to the data
# libary(gamlss)
# gamlss(Ni~1,family=DEL, control=gamlss.control(n.cyc=50))
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